{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62a174b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:49:37.587970Z",
     "iopub.status.busy": "2023-05-24T01:49:37.587480Z",
     "iopub.status.idle": "2023-05-24T01:49:47.813097Z",
     "shell.execute_reply": "2023-05-24T01:49:47.812110Z"
    },
    "executionInfo": {
     "elapsed": 2844,
     "status": "ok",
     "timestamp": 1684409199330,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "wHsFZo6akD8M",
    "outputId": "d017ea14-1ff6-429f-d7da-2f6ad9e82ec1",
    "papermill": {
     "duration": 10.243224,
     "end_time": "2023-05-24T01:49:47.815926",
     "exception": false,
     "start_time": "2023-05-24T01:49:37.572702",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "batch_size = 8\n",
    "train_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\n",
    "\n",
    "test_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efa03e61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:49:47.826704Z",
     "iopub.status.busy": "2023-05-24T01:49:47.826217Z",
     "iopub.status.idle": "2023-05-24T01:49:48.223168Z",
     "shell.execute_reply": "2023-05-24T01:49:48.222044Z"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684409199330,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "VjkCK8NykPMb",
    "outputId": "d8b6db1d-275c-4979-f5c6-411c763dea3f",
    "papermill": {
     "duration": 0.406156,
     "end_time": "2023-05-24T01:49:48.226992",
     "exception": false,
     "start_time": "2023-05-24T01:49:47.820836",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(train_loader)\n",
    "# images, labels = dataiter.next()\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d508e06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:49:48.240137Z",
     "iopub.status.busy": "2023-05-24T01:49:48.239799Z",
     "iopub.status.idle": "2023-05-24T01:49:48.273734Z",
     "shell.execute_reply": "2023-05-24T01:49:48.272896Z"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684409199331,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "1mY2iwmCkcpA",
    "papermill": {
     "duration": 0.043227,
     "end_time": "2023-05-24T01:49:48.275783",
     "exception": false,
     "start_time": "2023-05-24T01:49:48.232556",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.nn import init\n",
    "\n",
    "def conv3x3(in_planes, out_planes, stride=1):\n",
    "    \"3x3 convolution with padding\"\n",
    "    return nn.Conv2d(\n",
    "        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
    "    )\n",
    "\n",
    "\n",
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(\n",
    "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
    "    ):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = conv3x3(inplanes, planes, stride)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.conv2 = conv3x3(planes, planes)\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "        if use_triplet_attention:\n",
    "            self.triplet_attention = TripletAttention(planes, 16)\n",
    "        else:\n",
    "            self.triplet_attention = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        if not self.triplet_attention is None:\n",
    "            out = self.triplet_attention(out)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class Bottleneck(nn.Module):\n",
    "    expansion = 4\n",
    "\n",
    "    def __init__(\n",
    "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
    "    ):\n",
    "        super(Bottleneck, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
    "        )\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n",
    "        self.bn3 = nn.BatchNorm2d(planes * 4)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "        if use_triplet_attention:\n",
    "            self.triplet_attention = TripletAttention(planes * 4, 16)\n",
    "        else:\n",
    "            self.triplet_attention = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv3(out)\n",
    "        out = self.bn3(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        if not self.triplet_attention is None:\n",
    "            out = self.triplet_attention(out)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n",
    "        self.inplanes = 64\n",
    "        super(ResNet, self).__init__()\n",
    "        self.network_type = network_type\n",
    "        # different model config between ImageNet and CIFAR\n",
    "        if network_type == \"ImageNet\":\n",
    "            self.conv1 = nn.Conv2d(\n",
    "                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n",
    "            )\n",
    "            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "            self.avgpool = nn.AvgPool2d(7)\n",
    "        else:\n",
    "            self.conv1 = nn.Conv2d(\n",
    "                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n",
    "            )\n",
    "\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "\n",
    "        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n",
    "        self.layer2 = self._make_layer(\n",
    "            block, 128, layers[1], stride=2, att_type=att_type\n",
    "        )\n",
    "        self.layer3 = self._make_layer(\n",
    "            block, 256, layers[2], stride=2, att_type=att_type\n",
    "        )\n",
    "        self.layer4 = self._make_layer(\n",
    "            block, 512, layers[3], stride=2, att_type=att_type\n",
    "        )\n",
    "\n",
    "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
    "\n",
    "        init.kaiming_normal_(self.fc.weight)\n",
    "        for key in self.state_dict():\n",
    "            if key.split(\".\")[-1] == \"weight\":\n",
    "                if \"conv\" in key:\n",
    "                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n",
    "                if \"bn\" in key:\n",
    "                    if \"SpatialGate\" in key:\n",
    "                        self.state_dict()[key][...] = 0\n",
    "                    else:\n",
    "                        self.state_dict()[key][...] = 1\n",
    "            elif key.split(\".\")[-1] == \"bias\":\n",
    "                self.state_dict()[key][...] = 0\n",
    "\n",
    "    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n",
    "        downsample = None\n",
    "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
    "            downsample = nn.Sequential(\n",
    "                nn.Conv2d(\n",
    "                    self.inplanes,\n",
    "                    planes * block.expansion,\n",
    "                    kernel_size=1,\n",
    "                    stride=stride,\n",
    "                    bias=False,\n",
    "                ),\n",
    "                nn.BatchNorm2d(planes * block.expansion),\n",
    "            )\n",
    "\n",
    "        layers = []\n",
    "        layers.append(\n",
    "            block(\n",
    "                self.inplanes,\n",
    "                planes,\n",
    "                stride,\n",
    "                downsample,\n",
    "                use_triplet_attention=att_type == \"TripletAttention\",\n",
    "            )\n",
    "        )\n",
    "        self.inplanes = planes * block.expansion\n",
    "        for i in range(1, blocks):\n",
    "            layers.append(\n",
    "                block(\n",
    "                    self.inplanes,\n",
    "                    planes,\n",
    "                    use_triplet_attention=att_type == \"TripletAttention\",\n",
    "                )\n",
    "            )\n",
    "\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        if self.network_type == \"ImageNet\":\n",
    "            x = self.maxpool(x)\n",
    "\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "\n",
    "        if self.network_type == \"ImageNet\":\n",
    "            x = self.avgpool(x)\n",
    "        else:\n",
    "            x = F.avg_pool2d(x, 4)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e01f693",
   "metadata": {
    "papermill": {
     "duration": 0.005199,
     "end_time": "2023-05-24T01:49:48.286537",
     "exception": false,
     "start_time": "2023-05-24T01:49:48.281338",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 定义SKNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0de88875",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:49:48.298937Z",
     "iopub.status.busy": "2023-05-24T01:49:48.298640Z",
     "iopub.status.idle": "2023-05-24T01:49:48.327986Z",
     "shell.execute_reply": "2023-05-24T01:49:48.326805Z"
    },
    "papermill": {
     "duration": 0.038851,
     "end_time": "2023-05-24T01:49:48.330691",
     "exception": false,
     "start_time": "2023-05-24T01:49:48.291840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "#sknet\n",
    "class SKConv(nn.Module):\n",
    "    def __init__(self, features, M=2, G=32, r=16, stride=1 ,L=32):\n",
    "        \"\"\" Constructor\n",
    "        Args:\n",
    "            features: input channel dimensionality.\n",
    "            M: the number of branchs.\n",
    "            G: num of convolution groups.\n",
    "            r: the ratio for compute d, the length of z.\n",
    "            stride: stride, default 1.\n",
    "            L: the minimum dim of the vector z in paper, default 32.\n",
    "        \"\"\"\n",
    "        super(SKConv, self).__init__()\n",
    "        d = max(int(features/r), L)\n",
    "        self.M = M\n",
    "        self.features = features\n",
    "        self.convs = nn.ModuleList([])\n",
    "        for i in range(M):\n",
    "            self.convs.append(nn.Sequential(\n",
    "                nn.Conv2d(features, features, kernel_size=3, stride=stride, padding=1+i, dilation=1+i, groups=G, bias=False),\n",
    "                nn.BatchNorm2d(features),\n",
    "                nn.ReLU(inplace=True)\n",
    "            ))\n",
    "        self.gap = nn.AdaptiveAvgPool2d((1,1))\n",
    "        self.fc = nn.Sequential(nn.Conv2d(features, d, kernel_size=1, stride=1, bias=False),\n",
    "                                nn.BatchNorm2d(d),\n",
    "                                nn.ReLU(inplace=True))\n",
    "        self.fcs = nn.ModuleList([])\n",
    "        for i in range(M):\n",
    "            self.fcs.append(\n",
    "                 nn.Conv2d(d, features, kernel_size=1, stride=1)\n",
    "            )\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        batch_size = x.shape[0]\n",
    "        \n",
    "        feats = [conv(x) for conv in self.convs]      \n",
    "        feats = torch.cat(feats, dim=1)\n",
    "        feats = feats.view(batch_size, self.M, self.features, feats.shape[2], feats.shape[3])\n",
    "        \n",
    "        feats_U = torch.sum(feats, dim=1)\n",
    "        feats_S = self.gap(feats_U)\n",
    "        feats_Z = self.fc(feats_S)\n",
    "\n",
    "        attention_vectors = [fc(feats_Z) for fc in self.fcs]\n",
    "        attention_vectors = torch.cat(attention_vectors, dim=1)\n",
    "        attention_vectors = attention_vectors.view(batch_size, self.M, self.features, 1, 1)\n",
    "        attention_vectors = self.softmax(attention_vectors)\n",
    "        \n",
    "        feats_V = torch.sum(feats*attention_vectors, dim=1)\n",
    "        \n",
    "        return feats_V\n",
    "\n",
    "\n",
    "class SKUnit(nn.Module):\n",
    "    def __init__(self, in_features, mid_features, out_features, M=2, G=32, r=16, stride=1, L=32):\n",
    "        \"\"\" Constructor\n",
    "        Args:\n",
    "            in_features: input channel dimensionality.\n",
    "            out_features: output channel dimensionality.\n",
    "            M: the number of branchs.\n",
    "            G: num of convolution groups.\n",
    "            r: the ratio for compute d, the length of z.\n",
    "            mid_features: the channle dim of the middle conv with stride not 1, default out_features/2.\n",
    "            stride: stride.\n",
    "            L: the minimum dim of the vector z in paper.\n",
    "        \"\"\"\n",
    "        super(SKUnit, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_features, mid_features, 1, stride=1, bias=False),\n",
    "            nn.BatchNorm2d(mid_features),\n",
    "            nn.ReLU(inplace=True)\n",
    "            )\n",
    "        \n",
    "        self.conv2_sk = SKConv(mid_features, M=M, G=G, r=r, stride=stride, L=L)\n",
    "        \n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(mid_features, out_features, 1, stride=1, bias=False),\n",
    "            nn.BatchNorm2d(out_features)\n",
    "            )\n",
    "        \n",
    "\n",
    "        if in_features == out_features: # when dim not change, input_features could be added diectly to out\n",
    "            self.shortcut = nn.Sequential()\n",
    "        else: # when dim not change, input_features should also change dim to be added to out\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(in_features, out_features, 1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(out_features)\n",
    "            )\n",
    "        \n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "        \n",
    "        out = self.conv1(x)\n",
    "        out = self.conv2_sk(out)\n",
    "        out = self.conv3(out)\n",
    "        \n",
    "        return self.relu(out + self.shortcut(residual))\n",
    "\n",
    "class SKNet(nn.Module):\n",
    "    def __init__(self, class_num, nums_block_list = [3, 4, 6, 3], strides_list = [1, 2, 2, 2]):\n",
    "        super(SKNet, self).__init__()\n",
    "        self.basic_conv = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, 7, 2, 3, bias=False),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "        )\n",
    "        \n",
    "        self.maxpool = nn.MaxPool2d(3,2,1)\n",
    "        \n",
    "        self.stage_1 = self._make_layer(64, 128, 256, nums_block=nums_block_list[0], stride=strides_list[0])\n",
    "        self.stage_2 = self._make_layer(256, 256, 512, nums_block=nums_block_list[1], stride=strides_list[1])\n",
    "        self.stage_3 = self._make_layer(512, 512, 1024, nums_block=nums_block_list[2], stride=strides_list[2])\n",
    "        self.stage_4 = self._make_layer(1024, 1024, 2048, nums_block=nums_block_list[3], stride=strides_list[3])\n",
    "     \n",
    "        self.gap = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.classifier = nn.Linear(2048, class_num)\n",
    "        \n",
    "    def _make_layer(self, in_feats, mid_feats, out_feats, nums_block, stride=1):\n",
    "        layers=[SKUnit(in_feats, mid_feats, out_feats, stride=stride)]\n",
    "        for _ in range(1,nums_block):\n",
    "            layers.append(SKUnit(out_feats, mid_feats, out_feats))\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        fea = self.basic_conv(x)\n",
    "        fea = self.maxpool(fea)\n",
    "        fea = self.stage_1(fea)\n",
    "        fea = self.stage_2(fea)\n",
    "        fea = self.stage_3(fea)\n",
    "        fea = self.stage_4(fea)\n",
    "        fea = self.gap(fea)\n",
    "        fea = torch.squeeze(fea)\n",
    "        fea = self.classifier(fea)\n",
    "        return fea\n",
    "\n",
    "def SKNet26(nums_class=100):\n",
    "    return SKNet(nums_class, [2, 2, 2, 2])\n",
    "def SKNet50(nums_class=100):\n",
    "    return SKNet(nums_class, [3, 4, 6, 3])\n",
    "def SKNet101(nums_class=100):\n",
    "    return SKNet(nums_class, [3, 4, 23, 3])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49054228",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:49:48.343349Z",
     "iopub.status.busy": "2023-05-24T01:49:48.343046Z",
     "iopub.status.idle": "2023-05-24T01:49:51.395863Z",
     "shell.execute_reply": "2023-05-24T01:49:51.394901Z"
    },
    "executionInfo": {
     "elapsed": 680,
     "status": "ok",
     "timestamp": 1684414637992,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "otA9CIgdnXfW",
    "papermill": {
     "duration": 3.06171,
     "end_time": "2023-05-24T01:49:51.398298",
     "exception": false,
     "start_time": "2023-05-24T01:49:48.336588",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "device=\"cuda\"\n",
    "net=SKNet26().to(device)\n",
    "#net=ResidualNet(\"CIFAR100\",50,100,None).to(device)\n",
    "#print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b530ddf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:49:51.411328Z",
     "iopub.status.busy": "2023-05-24T01:49:51.410992Z",
     "iopub.status.idle": "2023-05-24T01:49:51.418336Z",
     "shell.execute_reply": "2023-05-24T01:49:51.417314Z"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684414638807,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "35VI01P_kqal",
    "outputId": "64747940-f10a-4982-df2f-d5bc5a500b78",
    "papermill": {
     "duration": 0.017642,
     "end_time": "2023-05-24T01:49:51.421952",
     "exception": false,
     "start_time": "2023-05-24T01:49:51.404310",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.0005)\n",
    "print(len(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b117ae3",
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684414638807,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "ck0do7nrmNTQ",
    "papermill": {
     "duration": 0.005422,
     "end_time": "2023-05-24T01:49:51.433035",
     "exception": false,
     "start_time": "2023-05-24T01:49:51.427613",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88695f51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:49:51.445874Z",
     "iopub.status.busy": "2023-05-24T01:49:51.445590Z",
     "iopub.status.idle": "2023-05-24T01:49:51.457920Z",
     "shell.execute_reply": "2023-05-24T01:49:51.457025Z"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684414638808,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "xKlx2mCxlE02",
    "papermill": {
     "duration": 0.021279,
     "end_time": "2023-05-24T01:49:51.459915",
     "exception": false,
     "start_time": "2023-05-24T01:49:51.438636",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "def train(epoch):\n",
    "    net.train()\n",
    "    # Loop over each batch from the training set\n",
    "    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n",
    "    for batch_idx, (data, target) in enumerate(train_tqdm):\n",
    "        # Copy data to GPU if needed\n",
    "        data = data.to(device)\n",
    "        target = target.to(device)\n",
    "        # Zero gradient buffers\n",
    "        optimizer.zero_grad()\n",
    "        # Pass data through the network\n",
    "        output = net(data)\n",
    "        # Calculate loss\n",
    "        loss = criterion(output, target)\n",
    "        # Backpropagate\n",
    "        loss.backward()\n",
    "        # Update weights\n",
    "        optimizer.step()  # w - alpha * dL / dw\n",
    "        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n",
    "\n",
    "def validate(lossv,top1AccuracyList,top5AccuracyList):\n",
    "    net.eval()\n",
    "    val_loss = 0\n",
    "    top1Correct = 0\n",
    "    top5Correct = 0\n",
    "    for index,(data, target) in enumerate(test_loader):\n",
    "        data = data.to(device)\n",
    "        labels = target.to(device)\n",
    "        outputs = net(data)\n",
    "        val_loss += criterion(outputs, labels).data.item()\n",
    "        _, top1Predicted = torch.max(outputs.data, 1)\n",
    "        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n",
    "        top1Correct += (top1Predicted == labels).cpu().sum().item()\n",
    "        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n",
    "        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n",
    "\n",
    "    val_loss /= len(test_loader)\n",
    "    lossv.append(val_loss)\n",
    "\n",
    "    top1Acc=100*top1Correct / len(test_loader.dataset)\n",
    "    top5Acc=100*top5Correct / len(test_loader.dataset)\n",
    "    top1AccuracyList.append(top1Acc)\n",
    "    top5AccuracyList.append(top5Acc)\n",
    "    \n",
    "    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n",
    "#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n",
    "#     accv.append(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b64abf15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:49:51.472626Z",
     "iopub.status.busy": "2023-05-24T01:49:51.472106Z",
     "iopub.status.idle": "2023-05-24T03:00:06.664972Z",
     "shell.execute_reply": "2023-05-24T03:00:06.663182Z"
    },
    "id": "pTWz8B3Dl9gu",
    "outputId": "b7c77e1e-40cc-417b-f5de-7467800712dd",
    "papermill": {
     "duration": 4215.201632,
     "end_time": "2023-05-24T03:00:06.667188",
     "exception": false,
     "start_time": "2023-05-24T01:49:51.465556",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|██████████| 5000/5000 [03:20<00:00, 24.91it/s, loss=4.51]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 4.4227, Top1Accuracy: 986/10000 (10%) Top5Accuracy: 2886.0/10000 (29%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████| 5000/5000 [03:05<00:00, 26.94it/s, loss=3.64]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.7030, Top1Accuracy: 1379/10000 (14%) Top5Accuracy: 3885.0/10000 (39%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|██████████| 5000/5000 [03:06<00:00, 26.87it/s, loss=3.93]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.5476, Top1Accuracy: 1666/10000 (17%) Top5Accuracy: 4203.0/10000 (42%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|██████████| 5000/5000 [03:07<00:00, 26.66it/s, loss=2.33]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.3474, Top1Accuracy: 2053/10000 (21%) Top5Accuracy: 4868.0/10000 (49%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|██████████| 5000/5000 [03:08<00:00, 26.46it/s, loss=3.56]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.3183, Top1Accuracy: 2391/10000 (24%) Top5Accuracy: 5146.0/10000 (51%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|██████████| 5000/5000 [03:10<00:00, 26.28it/s, loss=2.61]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.5750, Top1Accuracy: 2682/10000 (27%) Top5Accuracy: 5515.0/10000 (55%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|██████████| 5000/5000 [03:14<00:00, 25.71it/s, loss=2.23]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.2811, Top1Accuracy: 2814/10000 (28%) Top5Accuracy: 5590.0/10000 (56%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|██████████| 5000/5000 [03:13<00:00, 25.85it/s, loss=3.6]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.5381, Top1Accuracy: 2911/10000 (29%) Top5Accuracy: 5730.0/10000 (57%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|██████████| 5000/5000 [03:23<00:00, 24.62it/s, loss=2.35]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 5.9080, Top1Accuracy: 2919/10000 (29%) Top5Accuracy: 5610.0/10000 (56%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|██████████| 5000/5000 [03:08<00:00, 26.54it/s, loss=2.8]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.3397, Top1Accuracy: 3090/10000 (31%) Top5Accuracy: 6022.0/10000 (60%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|██████████| 5000/5000 [03:09<00:00, 26.33it/s, loss=2.81]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 4.5550, Top1Accuracy: 3201/10000 (32%) Top5Accuracy: 5986.0/10000 (60%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|██████████| 5000/5000 [03:10<00:00, 26.25it/s, loss=2.92]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.3565, Top1Accuracy: 3149/10000 (31%) Top5Accuracy: 6017.0/10000 (60%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|██████████| 5000/5000 [03:11<00:00, 26.09it/s, loss=2.03]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 5.7384, Top1Accuracy: 3143/10000 (31%) Top5Accuracy: 5955.0/10000 (60%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|██████████| 5000/5000 [03:15<00:00, 25.59it/s, loss=2.3]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 5.9786, Top1Accuracy: 3372/10000 (34%) Top5Accuracy: 6108.0/10000 (61%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|██████████| 5000/5000 [03:13<00:00, 25.90it/s, loss=1.28]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.5640, Top1Accuracy: 3311/10000 (33%) Top5Accuracy: 6174.0/10000 (62%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15: 100%|██████████| 5000/5000 [03:14<00:00, 25.72it/s, loss=2.53]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.7113, Top1Accuracy: 3428/10000 (34%) Top5Accuracy: 6333.0/10000 (63%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16: 100%|██████████| 5000/5000 [03:14<00:00, 25.75it/s, loss=2.33]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 4.1065, Top1Accuracy: 3354/10000 (34%) Top5Accuracy: 6212.0/10000 (62%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17: 100%|██████████| 5000/5000 [03:12<00:00, 25.95it/s, loss=2.06]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 9.9441, Top1Accuracy: 3287/10000 (33%) Top5Accuracy: 5995.0/10000 (60%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18: 100%|██████████| 5000/5000 [03:06<00:00, 26.75it/s, loss=1.54]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 9.8740, Top1Accuracy: 3251/10000 (33%) Top5Accuracy: 5945.0/10000 (59%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|██████████| 5000/5000 [03:10<00:00, 26.23it/s, loss=2.25]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 5.2831, Top1Accuracy: 3507/10000 (35%) Top5Accuracy: 6286.0/10000 (63%)\n",
      "\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "lossv = []\n",
    "top1AccuracyList = []   # top1准确率列表\n",
    "top5AccuracyList = []   # top5准确率列表\n",
    "max_epoch = 20\n",
    "for epoch in range(max_epoch):  # loop over the dataset multiple times\n",
    "    train(epoch)\n",
    "    with torch.no_grad():\n",
    "        validate(lossv,top1AccuracyList,top5AccuracyList)\n",
    "        \n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "70c71b14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T03:00:28.072821Z",
     "iopub.status.busy": "2023-05-24T03:00:28.072454Z",
     "iopub.status.idle": "2023-05-24T03:00:28.076962Z",
     "shell.execute_reply": "2023-05-24T03:00:28.076010Z"
    },
    "executionInfo": {
     "elapsed": 22810,
     "status": "ok",
     "timestamp": 1684424352765,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "Hag0zV9cmHm_",
    "outputId": "1434ac5c-f76c-4637-9223-e6955c942441",
    "papermill": {
     "duration": 11.067646,
     "end_time": "2023-05-24T03:00:28.078925",
     "exception": false,
     "start_time": "2023-05-24T03:00:17.011279",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c16fc154",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T03:00:48.728926Z",
     "iopub.status.busy": "2023-05-24T03:00:48.727957Z",
     "iopub.status.idle": "2023-05-24T03:00:49.461892Z",
     "shell.execute_reply": "2023-05-24T03:00:49.460995Z"
    },
    "id": "Z1-D3LkhvzDq",
    "papermill": {
     "duration": 11.043967,
     "end_time": "2023-05-24T03:00:49.464130",
     "exception": false,
     "start_time": "2023-05-24T03:00:38.420163",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'validation top5 accuracy')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# 绘制曲线\n",
    "plt.figure(figsize=(5, 3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), lossv)\n",
    "plt.title('validation loss')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), top1AccuracyList)\n",
    "plt.title('validation top1 accuracy')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), top5AccuracyList)\n",
    "plt.title('validation top5 accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdd13dd9",
   "metadata": {
    "id": "fyWGvlbHup5w",
    "papermill": {
     "duration": 10.32185,
     "end_time": "2023-05-24T03:01:10.704155",
     "exception": false,
     "start_time": "2023-05-24T03:01:00.382305",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 4316.470158,
   "end_time": "2023-05-24T03:01:23.893463",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-05-24T01:49:27.423305",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
